Adaptive inverse control
Brief paper: Nonlinear multivariable adaptive control using multiple models and neural networks
Automatica (Journal of IFAC)
A Recurrent Fuzzy-Network-Based Inverse Modeling Method for a Temperature System Control
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
A novel neural approximate inverse control for unknown nonlinear discrete dynamical systems
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
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A compound neural network (CNN) which includes a linear feed-forward neural network (LFNN) and a recurrent neural network (RNN) is constructed to identify nonaffine dynamic nonlinear systems. Because the current control input is not included in the input vector of the recurrent neural network, output feedback control laws of nonlinear systems can be easily obtained from one-step predictive models approximated by the CNN. To minimize the predictive error, the current approximation error is used in the predictive process. The computation work is small because no on-line training is required for the output feedback controller. This algorithm can be used to SISO and MIMO nonlinear system control in real time. Simulation studies have shown that this scheme is simple and has good control accuracy and robustness.